Investigation of G007-LK treatment in mouse and human melanoma cell lines
Group - 24
Introduction
WNT/β-catenin pathway -> disregulation in melanoma
G007LK -> decreases WNT/β-catenin signaling
Two data sets and two metadata sets (mouse and human)
Goal was to identify up and down regulated genes after G007LK treatment
Methods
Cleaning
How we cleaned the data and why
zero_replacement <-function(zeroes_removed_df, cols_to_join){# Replace the zero values in the dataset with a calculated minimum-non-zero value per observation multiplied by 0.65. Thus we ensure that the 0 values that cannot undergo log transformation are replaced by a small non-zero value.zeroes_replaced_df <- zeroes_removed_df |>rowwise() |>mutate(min_non_zero =min(c_across (where (is.numeric))[c_across(where (is.numeric)) !=0], na.rm =TRUE)) |>ungroup() |>mutate(across (where (is.numeric), ~ifelse(. ==0, 0.65* min_non_zero, .))) |>select(-min_non_zero)#This part calculated the sum across all genes for every observation in the dataset with the replaced zeroeszeroes_replaced_sums_df <- zeroes_replaced_df |>rowwise() |>mutate(total_sum =sum(c_across (everything()),na.rm =TRUE))n =1000000# the data are normalized in TPM that is why we selected 1 million as n#Data normalization. This part ensures that different experiments with varying total expression levels are brought to consistant scalescaled_data_df <- zeroes_replaced_sums_df |>mutate(scaling_factor = n / total_sum) |>rowwise() |>mutate(across(-c(total_sum, scaling_factor), ~ . * scaling_factor)) |>select(-total_sum, -scaling_factor)scaled_data_df |>rowwise() |>mutate(row_sums =sum(c_across (everything()), na.rm =TRUE))#This part of the code binds the normalized and scaled expression data with the first columns from the original df data.joined_df <-bind_cols(cols_to_join, scaled_data_df)print(joined_df)}
Augmentation
Zero replacement
log2 fold change
Description
Observed major zero expression levels through cell lines in each of treatment
Description
Results
Mouse gene expression analysis
Only 346 significantly regulated genes out of 39075
0 handling making estimated treatment impact on expressions unsure.
Analysis of regulated Mouse genes across the 3 treatments
Approximately equal amounts of significantly regulated genes in each treatment
However, the overlap is very small
Discussion
The Human Analysis
PCA.1 The way the cell lines are segregated indicates that the biological background outweighs the direct impact of the treatment in regards to gene expression variance
PCA.2 Outliers like SkMEL28 and A375, along with the distinctive positioning of WM852, suggested unique gene expression profiles potentially associated with the response to G007.LK.
This limitation in explaining variance by the principal components underscores the complex nature of gene expression changes in the human cell lines.
Discussion
In our mouse model, the BVES gene appears to be the most significantly impacted.This gene contributes to regeneration processes
This transition between species allowed us to explore cross-species insights in regards to the effect of the G007.LK compound
More extensive work is needed to fully understand the impact of the G007.LK in gene expression and cellular pathway across different melanoma cells
References
Jo Waaler (2022) Human melanoma cell lines treated with G007-LK. BioStudies, E-MTAB-8438. Retrieved from https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-8438
Jo Waaler (2021). Mouse melanoma cell line B16-F10 treated with G007-LK. BioStudies, E-MTAB-8101. Retrieved from https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-8101
Waaler, Jo, et al. “Tankyrase Inhibition Sensitizes Melanoma to PD-1 Immune Checkpoint Blockade in Syngeneic Mouse Models.” Communications Biology, vol. 3, no. 1, 24 Apr. 2020, https://doi.org/10.1038%2Fs42003-020-0916-2 Accessed 25 Nov. 2023
Martín‐Fernández, Josep Antoni, Javier Palarea‐Albaladejo, and Ricardo Antonio Olea. “Dealing with zeros.” Compositional data analysis: Theory and applications (2011): 43-58.